Florent Aubry

1.2k total citations
34 papers, 861 citations indexed

About

Florent Aubry is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Florent Aubry has authored 34 papers receiving a total of 861 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Radiology, Nuclear Medicine and Imaging, 10 papers in Artificial Intelligence and 7 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Florent Aubry's work include Medical Imaging Techniques and Applications (6 papers), Radiomics and Machine Learning in Medical Imaging (5 papers) and Semantic Web and Ontologies (5 papers). Florent Aubry is often cited by papers focused on Medical Imaging Techniques and Applications (6 papers), Radiomics and Machine Learning in Medical Imaging (5 papers) and Semantic Web and Ontologies (5 papers). Florent Aubry collaborates with scholars based in France, United Kingdom and Netherlands. Florent Aubry's co-authors include Pierre Celsis, Jean‐François Démonet, I. Berry, R. Di Paola, Michèle Puel, Jérémie Pariente, Jean‐Claude Fort, Jean-Albert Lotterie, J. Bazin and J.Y. Herry and has published in prestigious journals such as PLoS ONE, NeuroImage and Brain.

In The Last Decade

Florent Aubry

33 papers receiving 827 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Florent Aubry France 10 332 294 196 132 99 34 861
Wenbo Zhang China 18 155 0.5× 285 1.0× 218 1.1× 178 1.3× 49 0.5× 74 1.1k
Randy Summers Canada 19 230 0.7× 474 1.6× 57 0.3× 68 0.5× 85 0.9× 38 1.2k
Daniel Schwarz Czechia 17 204 0.6× 272 0.9× 128 0.7× 68 0.5× 22 0.2× 99 887
Benjamin Kandel United States 10 333 1.0× 294 1.0× 179 0.9× 117 0.9× 20 0.2× 11 855
Robert Dann United States 15 589 1.8× 293 1.0× 163 0.8× 142 1.1× 19 0.2× 30 1.3k
Nieves Vélez de Mendizábal United States 19 397 1.2× 661 2.2× 146 0.7× 90 0.7× 28 0.3× 40 1.7k
Andrea T. Shafer United States 18 434 1.3× 417 1.4× 148 0.8× 112 0.8× 32 0.3× 55 1.2k
Manuel Gómez-Río Spain 19 264 0.8× 181 0.6× 221 1.1× 115 0.9× 16 0.2× 56 1.2k
Ani Eloyan United States 14 190 0.6× 444 1.5× 189 1.0× 51 0.4× 53 0.5× 42 761
Deepthi Rajashekar Canada 14 126 0.4× 134 0.5× 137 0.7× 55 0.4× 43 0.4× 35 636

Countries citing papers authored by Florent Aubry

Since Specialization
Citations

This map shows the geographic impact of Florent Aubry's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Florent Aubry with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Florent Aubry more than expected).

Fields of papers citing papers by Florent Aubry

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Florent Aubry. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Florent Aubry. The network helps show where Florent Aubry may publish in the future.

Co-authorship network of co-authors of Florent Aubry

This figure shows the co-authorship network connecting the top 25 collaborators of Florent Aubry. A scholar is included among the top collaborators of Florent Aubry based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Florent Aubry. Florent Aubry is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Gros, H., et al.. (2017). Comparison of BOLD, diffusion-weighted fMRI and ADC-fMRI for stimulation of the primary visual system with a block paradigm. Magnetic Resonance Imaging. 39. 123–131. 11 indexed citations
2.
Silva, Stein, Patrice Péran, Nicolas Chauveau, et al.. (2017). Brain Gray Matter MRI Morphometry for Neuroprognostication After Cardiac Arrest. Critical Care Medicine. 45(8). e763–e771. 23 indexed citations
3.
Black, David, L. Misery, Florent Aubry, et al.. (2011). Validation of a Model of Itch Induction for Brain Positron Emission Tomography Studies Using Histamine Iontophoresis. Acta Dermato Venereologica. 91(5). 504–510. 11 indexed citations
4.
Aubry, Florent, Jérémie Pariente, Jean-Albert Lotterie, et al.. (2009). Early diagnosis of Alzheimer's disease using cortical thickness: impact of cognitive reserve. Brain. 132(8). 2036–2047. 313 indexed citations
5.
Lévy, Jonathan, Cyril Pernet, Kader Boulanouar, et al.. (2009). Testing for the Dual-Route Cascade Reading Model in the Brain: An fMRI Effective Connectivity Account of an Efficient Reading Style. PLoS ONE. 4(8). e6675–e6675. 91 indexed citations
6.
Chauveau, Nicolas, et al.. (2008). Cortical Imaging on a Head Template: A Simulation Study Using a Resistor Mesh Model (RMM). Brain Topography. 21(1). 52–60. 4 indexed citations
7.
Lévy, Jonathan, Cyril Pernet, Kader Boulanouar, et al.. (2008). Piecemeal recruitment of left-lateralized brain areas during reading: A spatio-functional account. NeuroImage. 43(3). 581–591. 44 indexed citations
8.
Pariente, Jérémie, et al.. (2008). IC‐P2‐126: MRI‐based cortical thickness measurement improves the prediction of MCI to AD conversion. Alzheimer s & Dementia. 4(4S_Part_2). 1 indexed citations
9.
Aubry, Florent, et al.. (2007). Stimulus complexity and prospective timing: Clues for a parallel process model of time perception. Acta Psychologica. 128(1). 63–74. 17 indexed citations
10.
Aubry, Florent, et al.. (2000). [Towards the integration of the digital medical image folder within the computerized patient folder: PACS and image networks].. PubMed. 56(2). 140–55. 2 indexed citations
11.
Buvat, Irène, Florent Aubry, Georges El Fakhri, et al.. (1999). <title>The need to develop guidelines for the evaluation of medical image processing procedures</title>. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 3661. 1466–1477. 9 indexed citations
12.
Gibaud, Bernard, et al.. (1998). Standardization in the field of medical image management: the contribution of the MIMOSA model. IEEE Transactions on Medical Imaging. 17(1). 62–73. 8 indexed citations
13.
Aubry, Florent, et al.. (1996). A medical image object-oriented database with image processing and automatic reorganization capabilities. Computerized Medical Imaging and Graphics. 20(4). 315–331. 7 indexed citations
14.
Aubry, Florent, et al.. (1996). [Management and transmission of medical images. Main standards and norms].. PubMed. 77(11). 1105–20. 1 indexed citations
15.
Gibaud, Bernard, et al.. (1994). MIMOSA: A functional model of the management of medical images. Medical Informatics. 19(2). 95–108. 2 indexed citations
16.
Badaoui, Saloua, et al.. (1993). A database manager of biomedical images. Medical Informatics. 18(1). 23–33. 8 indexed citations
17.
Bizais, Y., et al.. (1991). <title>Qualitative approach to medical image databases</title>. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 1446. 156–167. 1 indexed citations
18.
Aubry, Florent, et al.. (1991). <title>Object-oriented model for medical image database</title>. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 1446. 168–176. 3 indexed citations
19.
Aubry, Florent, Saloua Badaoui, H. Kaplan, & R. Di Paola. (1988). Design and implementation of a biomedical image database (BDIM). Medical Informatics. 13(4). 241–248. 6 indexed citations
20.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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